An Empirical Study of Neural Networks for Trend Detection in Time Series

Miot, Alexandre; Drigout, Gilles

doi:10.1007/s42979-020-00362-1

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…Previous work has studied trend detection in online social media platforms such as Twitter and Facebook (Benhardus and Kalita, 2013;Mathioudakis and Koudas, 2010;Miot and Drigout, 2020). Benhardus and Kalita (2013) outlined the methodologies for using the data from online platforms and proposed criteria based on the frequency of words to identify trending topics in Twitter.…”

Section: Related Workmentioning

confidence: 99%

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Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp

Chen¹,

Neves²,

Solorio³

2021

Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

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Performance of neural models for named entity recognition degrades over time, becoming stale. This degradation is due to temporal drift, the change in our target variables' statistical properties over time. This issue is especially problematic for social media data, where topics change rapidly. In order to mitigate the problem, data annotation and retraining of models is common. Despite its usefulness, this process is expensive and timeconsuming, which motivates new research on efficient model updating. In this paper, we propose an intuitive approach to measure the potential trendiness of tweets and use this metric to select the most informative instances to use for training. We conduct experiments on three state-of-the-art models on the Temporal Twitter Dataset. Our approach shows larger increases in prediction accuracy with less training data than the alternatives, making it an attractive, practical solution. 11 We release the code at https://github.com/ RiTUAL-UH/trending_NER.

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Section: Related Workmentioning

confidence: 99%

“…Mathioudakis and Koudas (2010) presented a system to detect bursty keywords that suddenly appear in tweets at an unusually high rate. Recently, Miot and Drigout (2020) investigated the efficiency of deep neural networks to detect trends. However, these techniques are applied without taking named entities into consideration.…”

Section: Related Workmentioning

confidence: 99%

Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp

Chen¹,

Neves²,

Solorio³

2021

Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

2021

View full text Add to dashboard Cite

The Moral Foundation Theory suggests five moral foundations that can capture the view of a user on a particular issue. It is widely used to identify sentence-level sentiment. In this paper, we study the nuanced stances and partisan sentiment towards entities of US politicians using Moral Foundation Theory, on two politically divisive issues -Gun Control and Immigration. We define the nuanced stances of the US politicians on these two topics by the grades given by related organizations to the politicians. To conduct this study, we first filter out 74k and 87k tweets on the topics Gun Control and Immigration, respectively, from an existing tweet corpus authored by US parliament members. Then, we identify moral foundations in these tweets using deep relational learning. Finally, doing qualitative and quantitative evaluations on this dataset, we found out that there is a strong correlation between moral foundation usage and politicians' nuanced stances on a particular topic. We also found notable differences in moral foundation usage by different political parties when they address different entities. c : SameIdeology(t1, t2) ∧ SameTopic(t1, t2)∧ SameTime(t1, t2) ∧ HasMF(t1, m) ⇒ HasMF(t2, m) 10 Rujun Han, Qiang Ning, and Nanyun Peng. 2019. Joint event and temporal relation extraction with shared representations and structured prediction. In

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